Fill in missing values with nearest neighbour in Python numpy masked arrays?

Question

I am working with a 2D Numpy masked_array in Python. I need to change the data values in the masked area such that they equal the nearest unmasked value.

NB. If there ar

Answer 1

I generally use a distance transform, as wisely suggested by Juh_ in this question.

This does not directly apply to masked arrays, but I do not think it will be that hard to transpose there, and it is quite efficient, I've had no problem applying it to large 100MPix images.

Copying the relevant method there for reference :

import numpy as np
from scipy import ndimage as nd

def fill(data, invalid=None):
    """
    Replace the value of invalid 'data' cells (indicated by 'invalid') 
    by the value of the nearest valid data cell

    Input:
        data:    numpy array of any dimension
        invalid: a binary array of same shape as 'data'. True cells set where data
                 value should be replaced.
                 If None (default), use: invalid  = np.isnan(data)

    Output: 
        Return a filled array. 
    """
    #import numpy as np
    #import scipy.ndimage as nd

    if invalid is None: invalid = np.isnan(data)

    ind = nd.distance_transform_edt(invalid, return_distances=False, return_indices=True)
    return data[tuple(ind)]